[Frontend][2/n] Make pooling entrypoints request schema consensus | ChatRequest (#32574)

Signed-off-by: wang.yuqi <yuqi.wang@daocloud.io>

docs/design/io_processor_plugins.md

@@ -79,7 +79,7 @@ The `post_process*` methods take `PoolingRequestOutput` objects as input and gen
 The `validate_or_generate_params` method is used for validating with the plugin any `SamplingParameters`/`PoolingParameters` received with the user request, or to generate new ones if none are specified. The function always returns the validated/generated parameters.
 The `output_to_response` method is used only for online serving and converts the plugin output to the `IOProcessorResponse` type that is then returned by the API Server. The implementation of the `/pooling` serving endpoint is available here [vllm/entrypoints/openai/serving_pooling.py](../../vllm/entrypoints/pooling/pooling/serving.py).
 
-An example implementation of a plugin that enables generating geotiff images with the PrithviGeospatialMAE model is available [here](https://github.com/IBM/terratorch/tree/main/terratorch/vllm/plugins/segmentation). Please, also refer to our online ([examples/pooling/plugin/prithvi_geospatial_mae_client.py](../../examples/pooling/plugin/prithvi_geospatial_mae_client.py)) and offline ([examples/pooling/plugin/prithvi_geospatial_mae_io_processor.py](../../examples/pooling/plugin/prithvi_geospatial_mae_io_processor.py)) inference examples.
+An example implementation of a plugin that enables generating geotiff images with the PrithviGeospatialMAE model is available [here](https://github.com/IBM/terratorch/tree/main/terratorch/vllm/plugins/segmentation). Please, also refer to our online ([examples/pooling/plugin/prithvi_geospatial_mae_online.py](../../examples/pooling/plugin/prithvi_geospatial_mae_online.py)) and offline ([examples/pooling/plugin/prithvi_geospatial_mae_io_processor.py](../../examples/pooling/plugin/prithvi_geospatial_mae_io_processor.py)) inference examples.
 
 ## Using an IO Processor plugin
 

docs/models/pooling_models.md

@@ -273,7 +273,7 @@ outputs = llm.embed(
 print(outputs[0].outputs)
 ```
 
-A code example can be found here: [examples/pooling/embed/embed_matryoshka_fy.py](../../examples/pooling/embed/embed_matryoshka_fy.py)
+A code example can be found here: [examples/pooling/embed/embed_matryoshka_fy_offline.py](../../examples/pooling/embed/embed_matryoshka_fy_offline.py)
 
 ### Online Inference
 
@@ -303,7 +303,7 @@ Expected output:
 {"id":"embd-5c21fc9a5c9d4384a1b021daccaf9f64","object":"list","created":1745476417,"model":"jinaai/jina-embeddings-v3","data":[{"index":0,"object":"embedding","embedding":[-0.3828125,-0.1357421875,0.03759765625,0.125,0.21875,0.09521484375,-0.003662109375,0.1591796875,-0.130859375,-0.0869140625,-0.1982421875,0.1689453125,-0.220703125,0.1728515625,-0.2275390625,-0.0712890625,-0.162109375,-0.283203125,-0.055419921875,-0.0693359375,0.031982421875,-0.04052734375,-0.2734375,0.1826171875,-0.091796875,0.220703125,0.37890625,-0.0888671875,-0.12890625,-0.021484375,-0.0091552734375,0.23046875]}],"usage":{"prompt_tokens":8,"total_tokens":8,"completion_tokens":0,"prompt_tokens_details":null}}
 ```
 
-An OpenAI client example can be found here: [examples/pooling/embed/openai_embedding_matryoshka_fy.py](../../examples/pooling/embed/openai_embedding_matryoshka_fy.py)
+An OpenAI client example can be found here: [examples/pooling/embed/openai_embedding_matryoshka_fy_client.py](../../examples/pooling/embed/openai_embedding_matryoshka_fy_client.py)
 
 ## Deprecated Features
 

docs/models/supported_models.md

@@ -619,7 +619,7 @@ These models primarily support the [`LLM.encode`](./pooling_models.md#llmencode)
 | `ModernBertForTokenClassification` | ModernBERT-based | `disham993/electrical-ner-ModernBERT-base` |  |  |
 
 !!! note
-    Named Entity Recognition (NER) usage, please refer to [examples/pooling/token_classify/ner.py](../../examples/pooling/token_classify/ner.py), [examples/pooling/token_classify/ner_client.py](../../examples/pooling/token_classify/ner_client.py).
+    Named Entity Recognition (NER) usage, please refer to [examples/pooling/token_classify/ner_offline.py](../../examples/pooling/token_classify/ner_offline.py), [examples/pooling/token_classify/ner_online.py](../../examples/pooling/token_classify/ner_online.py).
 
 ## List of Multimodal Language Models
 

docs/serving/openai_compatible_server.md

@@ -551,7 +551,7 @@ Our Pooling API encodes input prompts using a [pooling model](../models/pooling_
 
 The input format is the same as [Embeddings API](#embeddings-api), but the output data can contain an arbitrary nested list, not just a 1-D list of floats.
 
-Code example: [examples/pooling/pooling/openai_pooling_client.py](../../examples/pooling/pooling/openai_pooling_client.py)
+Code example: [examples/pooling/pooling/pooling_online.py](../../examples/pooling/pooling/pooling_online.py)
 
 ### Classification API
 

examples/pooling/embed/embedding_requests_base64_online.py

@@ -26,36 +26,42 @@ def post_http_request(prompt: dict, api_url: str) -> requests.Response:
 
 
 def parse_args():
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--host", type=str, default="localhost")
-    parser.add_argument("--port", type=int, default=8000)
-    parser.add_argument("--model", type=str, default="intfloat/e5-small")
-
-    return parser.parse_args()
+    parse = argparse.ArgumentParser()
+    parse.add_argument("--host", type=str, default="localhost")
+    parse.add_argument("--port", type=int, default=8000)
+    return parse.parse_args()
 
 
 def main(args):
-    api_url = f"http://{args.host}:{args.port}/v1/embeddings"
-    model_name = args.model
+    base_url = f"http://{args.host}:{args.port}"
+    models_url = base_url + "/v1/models"
+    embeddings_url = base_url + "/v1/embeddings"
+
+    response = requests.get(models_url)
+    model = response.json()["data"][0]["id"]
+
+    input_texts = [
+        "The best thing about vLLM is that it supports many different models",
+    ] * 2
 
     # The OpenAI client does not support the embed_dtype and endianness parameters.
     for embed_dtype in EMBED_DTYPE_TO_TORCH_DTYPE:
         for endianness in ENDIANNESS:
             prompt = {
-                "model": model_name,
-                "input": "vLLM is great!",
+                "model": model,
+                "input": input_texts,
                 "encoding_format": "base64",
                 "embed_dtype": embed_dtype,
                 "endianness": endianness,
             }
-            response = post_http_request(prompt=prompt, api_url=api_url)
+            response = post_http_request(prompt=prompt, api_url=embeddings_url)
 
             embedding = []
             for data in response.json()["data"]:
                 binary = base64.b64decode(data["embedding"])
                 tensor = binary2tensor(binary, (-1,), embed_dtype, endianness)
                 embedding.append(tensor.to(torch.float32))
-            embedding = torch.cat(embedding)
+            embedding = torch.stack(embedding)
             print(embed_dtype, endianness, embedding.shape)
 
 

examples/pooling/embed/embedding_requests_bytes_online.py

@@ -31,14 +31,18 @@ def parse_args():
     parser = argparse.ArgumentParser()
     parser.add_argument("--host", type=str, default="localhost")
     parser.add_argument("--port", type=int, default=8000)
-    parser.add_argument("--model", type=str, default="intfloat/e5-small")
 
     return parser.parse_args()
 
 
 def main(args):
-    api_url = f"http://{args.host}:{args.port}/v1/embeddings"
-    model_name = args.model
+    base_url = f"http://{args.host}:{args.port}"
+    models_url = base_url + "/v1/models"
+    embeddings_url = base_url + "/v1/embeddings"
+
+    response = requests.get(models_url)
+    model = response.json()["data"][0]["id"]
+
     embedding_size = 0
 
     input_texts = [
@@ -50,13 +54,13 @@ def main(args):
     for embed_dtype in EMBED_DTYPE_TO_TORCH_DTYPE:
         for endianness in ENDIANNESS:
             prompt = {
-                "model": model_name,
+                "model": model,
                 "input": input_texts,
                 "encoding_format": "bytes",
                 "embed_dtype": embed_dtype,
                 "endianness": endianness,
             }
-            response = post_http_request(prompt=prompt, api_url=api_url)
+            response = post_http_request(prompt=prompt, api_url=embeddings_url)
             metadata = json.loads(response.headers["metadata"])
             body = response.content
             items = [MetadataItem(**x) for x in metadata["data"]]
@@ -73,13 +77,13 @@ def main(args):
     for embed_dtype in EMBED_DTYPE_TO_TORCH_DTYPE:
         for endianness in ENDIANNESS:
             prompt = {
-                "model": model_name,
+                "model": model,
                 "input": input_texts,
                 "encoding_format": "bytes_only",
                 "embed_dtype": embed_dtype,
                 "endianness": endianness,
             }
-            response = post_http_request(prompt=prompt, api_url=api_url)
+            response = post_http_request(prompt=prompt, api_url=embeddings_url)
             body = response.content
 
             items = build_metadata_items(

examples/pooling/pooling/pooling_online.py

@@ -25,18 +25,21 @@ def parse_args():
     parser = argparse.ArgumentParser()
     parser.add_argument("--host", type=str, default="localhost")
     parser.add_argument("--port", type=int, default=8000)
-    parser.add_argument("--model", type=str, default="internlm/internlm2-1_8b-reward")
 
     return parser.parse_args()
 
 
 def main(args):
-    api_url = f"http://{args.host}:{args.port}/pooling"
-    model_name = args.model
+    base_url = f"http://{args.host}:{args.port}"
+    models_url = base_url + "/v1/models"
+    pooing_url = base_url + "/pooling"
+
+    response = requests.get(models_url)
+    model = response.json()["data"][0]["id"]
 
     # Input like Completions API
-    prompt = {"model": model_name, "input": "vLLM is great!"}
-    pooling_response = post_http_request(prompt=prompt, api_url=api_url)
+    prompt = {"model": model, "input": "vLLM is great!"}
+    pooling_response = post_http_request(prompt=prompt, api_url=pooing_url)
     print("-" * 50)
     print("Pooling Response:")
     pprint.pprint(pooling_response.json())
@@ -44,7 +47,7 @@ def main(args):
 
     # Input like Chat API
     prompt = {
-        "model": model_name,
+        "model": model,
         "messages": [
             {
                 "role": "user",
@@ -52,7 +55,7 @@ def main(args):
             }
         ],
     }
-    pooling_response = post_http_request(prompt=prompt, api_url=api_url)
+    pooling_response = post_http_request(prompt=prompt, api_url=pooing_url)
     print("Pooling Response:")
     pprint.pprint(pooling_response.json())
     print("-" * 50)

tests/entrypoints/pooling/classify/test_online.py

@@ -167,7 +167,8 @@ def test_truncate_prompt_tokens(server: RemoteOpenAIServer, model_name: str):
 
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
 def test_add_special_tokens(server: RemoteOpenAIServer, model_name: str):
-    # FIXME: The add_special_tokens parameter doesn't seem to be working.
+    # The add_special_tokens parameter doesn't seem to be working with this model.
+    # working with papluca/xlm-roberta-base-language-detection
     response = requests.post(
         server.url_for("classify"),
         json={"model": model_name, "input": input_text, "add_special_tokens": False},
@@ -184,7 +185,110 @@ def test_add_special_tokens(server: RemoteOpenAIServer, model_name: str):
 
 
 @pytest.mark.asyncio
-async def test_invocations(server: RemoteOpenAIServer):
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+async def test_chat_request(server: RemoteOpenAIServer, model_name: str):
+    messages = [
+        {
+            "role": "user",
+            "content": "The cat sat on the mat.",
+        },
+        {
+            "role": "assistant",
+            "content": "A feline was resting on a rug.",
+        },
+        {
+            "role": "user",
+            "content": "Stars twinkle brightly in the night sky.",
+        },
+    ]
+
+    # test chat request basic usage
+    response = requests.post(
+        server.url_for("classify"),
+        json={"model": model_name, "messages": messages},
+    )
+
+    response.raise_for_status()
+    output = ClassificationResponse.model_validate(response.json())
+
+    assert output.object == "list"
+    assert output.model == MODEL_NAME
+    assert len(output.data) == 1
+    assert hasattr(output.data[0], "label")
+    assert hasattr(output.data[0], "probs")
+    assert output.usage.prompt_tokens == 51
+
+    # test add_generation_prompt
+    response = requests.post(
+        server.url_for("classify"),
+        json={"model": model_name, "messages": messages, "add_generation_prompt": True},
+    )
+
+    response.raise_for_status()
+    output = ClassificationResponse.model_validate(response.json())
+
+    assert output.object == "list"
+    assert output.model == MODEL_NAME
+    assert len(output.data) == 1
+    assert hasattr(output.data[0], "label")
+    assert hasattr(output.data[0], "probs")
+    assert output.usage.prompt_tokens == 54
+
+    # test continue_final_message
+    response = requests.post(
+        server.url_for("classify"),
+        json={
+            "model": model_name,
+            "messages": messages,
+            "continue_final_message": True,
+        },
+    )
+
+    response.raise_for_status()
+    output = ClassificationResponse.model_validate(response.json())
+
+    assert output.object == "list"
+    assert output.model == MODEL_NAME
+    assert len(output.data) == 1
+    assert hasattr(output.data[0], "label")
+    assert hasattr(output.data[0], "probs")
+    assert output.usage.prompt_tokens == 49
+
+    # test add_special_tokens
+    # The add_special_tokens parameter doesn't seem to be working with this model.
+    response = requests.post(
+        server.url_for("classify"),
+        json={"model": model_name, "messages": messages, "add_special_tokens": True},
+    )
+
+    response.raise_for_status()
+    output = ClassificationResponse.model_validate(response.json())
+
+    assert output.object == "list"
+    assert output.model == MODEL_NAME
+    assert len(output.data) == 1
+    assert hasattr(output.data[0], "label")
+    assert hasattr(output.data[0], "probs")
+    assert output.usage.prompt_tokens == 51
+
+    # test continue_final_message with add_generation_prompt
+    response = requests.post(
+        server.url_for("classify"),
+        json={
+            "model": model_name,
+            "messages": messages,
+            "continue_final_message": True,
+            "add_generation_prompt": True,
+        },
+    )
+    assert (
+        "Cannot set both `continue_final_message` and `add_generation_prompt` to True."
+        in response.json()["error"]["message"]
+    )
+
+
+@pytest.mark.asyncio
+async def test_invocations_completion_request(server: RemoteOpenAIServer):
     request_args = {
         "model": MODEL_NAME,
         "input": input_text,
@@ -213,6 +317,48 @@ async def test_invocations(server: RemoteOpenAIServer):
         )
 
 
+@pytest.mark.asyncio
+async def test_invocations_chat_request(server: RemoteOpenAIServer):
+    messages = [
+        {
+            "role": "user",
+            "content": "The cat sat on the mat.",
+        },
+        {
+            "role": "assistant",
+            "content": "A feline was resting on a rug.",
+        },
+        {
+            "role": "user",
+            "content": "Stars twinkle brightly in the night sky.",
+        },
+    ]
+
+    request_args = {"model": MODEL_NAME, "messages": messages}
+
+    classification_response = requests.post(
+        server.url_for("classify"), json=request_args
+    )
+    classification_response.raise_for_status()
+
+    invocation_response = requests.post(
+        server.url_for("invocations"), json=request_args
+    )
+    invocation_response.raise_for_status()
+
+    classification_output = classification_response.json()
+    invocation_output = invocation_response.json()
+
+    assert classification_output.keys() == invocation_output.keys()
+    for classification_data, invocation_data in zip(
+        classification_output["data"], invocation_output["data"]
+    ):
+        assert classification_data.keys() == invocation_data.keys()
+        assert classification_data["probs"] == pytest.approx(
+            invocation_data["probs"], rel=0.01
+        )
+
+
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
 async def test_use_activation(server: RemoteOpenAIServer, model_name: str):

tests/entrypoints/pooling/embed/test_online.py

@@ -214,64 +214,6 @@ async def test_completion_request_batched(
     run_embedding_correctness_test(hf_model, input_texts, vllm_outputs)
 
 
-@pytest.mark.asyncio
-@pytest.mark.parametrize("model_name", [MODEL_NAME])
-async def test_conversation_embedding(
-    server: RemoteOpenAIServer, client: openai.AsyncOpenAI, model_name: str
-):
-    messages = [
-        {
-            "role": "user",
-            "content": "The cat sat on the mat.",
-        },
-        {
-            "role": "assistant",
-            "content": "A feline was resting on a rug.",
-        },
-        {
-            "role": "user",
-            "content": "Stars twinkle brightly in the night sky.",
-        },
-    ]
-
-    chat_response = requests.post(
-        server.url_for("v1/embeddings"),
-        json={
-            "model": model_name,
-            "messages": messages,
-            "encoding_format": "float",
-        },
-    )
-    chat_response.raise_for_status()
-    chat_embeddings = EmbeddingResponse.model_validate(chat_response.json())
-
-    tokenizer = get_tokenizer(tokenizer_name=model_name)
-    prompt = tokenizer.apply_chat_template(
-        messages,
-        chat_template=DUMMY_CHAT_TEMPLATE,
-        add_generation_prompt=True,
-        continue_final_message=False,
-        tokenize=False,
-    )
-    completion_response = await client.embeddings.create(
-        model=model_name,
-        input=prompt,
-        encoding_format="float",
-        # To be consistent with chat
-        extra_body={"add_special_tokens": False},
-    )
-    completion_embeddings = EmbeddingResponse.model_validate(
-        completion_response.model_dump(mode="json")
-    )
-
-    assert chat_embeddings.id is not None
-    assert completion_embeddings.id is not None
-    assert chat_embeddings.created <= completion_embeddings.created
-    assert chat_embeddings.model_dump(exclude={"id", "created"}) == (
-        completion_embeddings.model_dump(exclude={"id", "created"})
-    )
-
-
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
 async def test_truncate_prompt_tokens(client: openai.AsyncOpenAI, model_name: str):
@@ -350,7 +292,129 @@ async def test_truncate_prompt_tokens(client: openai.AsyncOpenAI, model_name: st
 
 
 @pytest.mark.asyncio
-async def test_invocations(server: RemoteOpenAIServer, client: openai.AsyncOpenAI):
+@pytest.mark.parametrize("model_name", [MODEL_NAME])
+async def test_chat_request(
+    server: RemoteOpenAIServer, client: openai.AsyncOpenAI, model_name: str
+):
+    messages = [
+        {
+            "role": "user",
+            "content": "The cat sat on the mat.",
+        },
+        {
+            "role": "assistant",
+            "content": "A feline was resting on a rug.",
+        },
+        {
+            "role": "user",
+            "content": "Stars twinkle brightly in the night sky.",
+        },
+    ]
+
+    # test chat request basic usage
+    chat_response = requests.post(
+        server.url_for("v1/embeddings"),
+        json={
+            "model": model_name,
+            "messages": messages,
+            "encoding_format": "float",
+        },
+    )
+    chat_response.raise_for_status()
+    chat_embeddings = EmbeddingResponse.model_validate(chat_response.json())
+
+    tokenizer = get_tokenizer(tokenizer_name=model_name)
+    prompt = tokenizer.apply_chat_template(
+        messages,
+        chat_template=DUMMY_CHAT_TEMPLATE,
+        add_generation_prompt=True,
+        continue_final_message=False,
+        tokenize=False,
+    )
+    completion_response = await client.embeddings.create(
+        model=model_name,
+        input=prompt,
+        encoding_format="float",
+        # To be consistent with chat
+        extra_body={"add_special_tokens": False},
+    )
+    completion_embeddings = EmbeddingResponse.model_validate(
+        completion_response.model_dump(mode="json")
+    )
+
+    assert chat_embeddings.id is not None
+    assert completion_embeddings.id is not None
+    assert chat_embeddings.created <= completion_embeddings.created
+    assert chat_embeddings.model_dump(exclude={"id", "created"}) == (
+        completion_embeddings.model_dump(exclude={"id", "created"})
+    )
+
+    # test add_generation_prompt
+    response = requests.post(
+        server.url_for("v1/embeddings"),
+        json={"model": model_name, "messages": messages, "add_generation_prompt": True},
+    )
+
+    response.raise_for_status()
+    output = EmbeddingResponse.model_validate(response.json())
+
+    assert output.object == "list"
+    assert len(output.data) == 1
+    assert output.model == MODEL_NAME
+    assert output.usage.prompt_tokens == 34
+
+    # test continue_final_message
+    response = requests.post(
+        server.url_for("v1/embeddings"),
+        json={
+            "model": model_name,
+            "messages": messages,
+            "continue_final_message": True,
+        },
+    )
+
+    response.raise_for_status()
+    output = EmbeddingResponse.model_validate(response.json())
+
+    assert output.object == "list"
+    assert len(output.data) == 1
+    assert output.model == MODEL_NAME
+    assert output.usage.prompt_tokens == 33
+
+    # test add_special_tokens
+    response = requests.post(
+        server.url_for("v1/embeddings"),
+        json={"model": model_name, "messages": messages, "add_special_tokens": True},
+    )
+
+    response.raise_for_status()
+    output = EmbeddingResponse.model_validate(response.json())
+
+    assert output.object == "list"
+    assert len(output.data) == 1
+    assert output.model == MODEL_NAME
+    assert output.usage.prompt_tokens == 36
+
+    # test continue_final_message with add_generation_prompt
+    response = requests.post(
+        server.url_for("v1/embeddings"),
+        json={
+            "model": model_name,
+            "messages": messages,
+            "continue_final_message": True,
+            "add_generation_prompt": True,
+        },
+    )
+    assert (
+        "Cannot set both `continue_final_message` and `add_generation_prompt` to True."
+        in response.json()["error"]["message"]
+    )
+
+
+@pytest.mark.asyncio
+async def test_invocations_completion_request(
+    server: RemoteOpenAIServer, client: openai.AsyncOpenAI
+):
     request_args = {
         "model": MODEL_NAME,
         "input": input_text,
@@ -381,7 +445,7 @@ async def test_invocations(server: RemoteOpenAIServer, client: openai.AsyncOpenA
 
 
 @pytest.mark.asyncio
-async def test_invocations_conversation(server: RemoteOpenAIServer):
+async def test_invocations_chat_request(server: RemoteOpenAIServer):
     messages = [
         {
             "role": "user",

tests/entrypoints/pooling/pooling/test_online.py

@@ -138,7 +138,7 @@ def test_completion_request_batched(server: RemoteOpenAIServer, model_name: str)
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
-async def test_conversation_pooling(server: RemoteOpenAIServer, model_name: str):
+async def test_chat_request(server: RemoteOpenAIServer, model_name: str):
     messages = [
         {
             "role": "user",
@@ -154,6 +154,7 @@ async def test_conversation_pooling(server: RemoteOpenAIServer, model_name: str)
         },
     ]
 
+    # test chat request basic usage
     chat_response = requests.post(
         server.url_for("pooling"),
         json={
@@ -193,6 +194,68 @@ async def test_conversation_pooling(server: RemoteOpenAIServer, model_name: str)
         completion_poolings.model_dump(exclude={"id", "created"})
     )
 
+    # test add_generation_prompt
+    response = requests.post(
+        server.url_for("pooling"),
+        json={"model": model_name, "messages": messages, "add_generation_prompt": True},
+    )
+
+    response.raise_for_status()
+    output = PoolingResponse.model_validate(response.json())
+
+    assert output.object == "list"
+    assert len(output.data) == 1
+    assert output.model == MODEL_NAME
+    assert output.usage.prompt_tokens == 33
+
+    # test continue_final_message
+    # The continue_final_message parameter doesn't seem to be working with this model.
+    response = requests.post(
+        server.url_for("pooling"),
+        json={
+            "model": model_name,
+            "messages": messages,
+            "continue_final_message": True,
+        },
+    )
+
+    response.raise_for_status()
+    output = PoolingResponse.model_validate(response.json())
+
+    assert output.object == "list"
+    assert len(output.data) == 1
+    assert output.model == MODEL_NAME
+    assert output.usage.prompt_tokens == 33
+
+    # test add_special_tokens
+    response = requests.post(
+        server.url_for("pooling"),
+        json={"model": model_name, "messages": messages, "add_special_tokens": True},
+    )
+
+    response.raise_for_status()
+    output = PoolingResponse.model_validate(response.json())
+
+    assert output.object == "list"
+    assert len(output.data) == 1
+    assert output.model == MODEL_NAME
+    assert output.usage.prompt_tokens == 34
+
+    # test continue_final_message with add_generation_prompt
+    response = requests.post(
+        server.url_for("pooling"),
+        json={
+            "model": model_name,
+            "messages": messages,
+            "continue_final_message": True,
+            "add_generation_prompt": True,
+        },
+    )
+    assert (
+        "Cannot set both `continue_final_message` and `add_generation_prompt` to True."
+        in response.json()["error"]["message"]
+    )
+
 
 @pytest.mark.asyncio
 @pytest.mark.parametrize("model_name", [MODEL_NAME])
@@ -430,7 +493,7 @@ async def test_params_not_supported(
 
 
 @pytest.mark.asyncio
-async def test_invocations(server: RemoteOpenAIServer):
+async def test_invocations_chat_request(server: RemoteOpenAIServer):
     request_args = {
         "model": MODEL_NAME,
         "input": input_text,
@@ -462,7 +525,7 @@ async def test_invocations(server: RemoteOpenAIServer):
 
 
 @pytest.mark.asyncio
-async def test_invocations_conversation(server: RemoteOpenAIServer):
+async def test_invocations_conversation_chat_request(server: RemoteOpenAIServer):
     messages = [
         {
             "role": "user",

vllm/entrypoints/pooling/base/protocol.py

@@ -2,10 +2,11 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 
-from typing import Annotated
+from typing import Annotated, Any
 
-from pydantic import Field
+from pydantic import Field, model_validator
 
+from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
 from vllm.entrypoints.openai.engine.protocol import OpenAIBaseModel
 from vllm.utils import random_uuid
 
@@ -44,3 +45,66 @@ class CompletionRequestMixin(OpenAIBaseModel):
             "the prompt."
         ),
     )
+
+
+class ChatRequestMixin(OpenAIBaseModel):
+    messages: list[ChatCompletionMessageParam]
+
+    add_generation_prompt: bool = Field(
+        default=False,
+        description=(
+            "If true, the generation prompt will be added to the chat template. "
+            "This is a parameter used by chat template in tokenizer config of the "
+            "model."
+        ),
+    )
+
+    continue_final_message: bool = Field(
+        default=False,
+        description=(
+            "If this is set, the chat will be formatted so that the final "
+            "message in the chat is open-ended, without any EOS tokens. The "
+            "model will continue this message rather than starting a new one. "
+            'This allows you to "prefill" part of the model\'s response for it. '
+            "Cannot be used at the same time as `add_generation_prompt`."
+        ),
+    )
+
+    add_special_tokens: bool = Field(
+        default=False,
+        description=(
+            "If true, special tokens (e.g. BOS) will be added to the prompt "
+            "on top of what is added by the chat template. "
+            "For most models, the chat template takes care of adding the "
+            "special tokens so this should be set to false (as is the "
+            "default)."
+        ),
+    )
+
+    chat_template: str | None = Field(
+        default=None,
+        description=(
+            "A Jinja template to use for this conversion. "
+            "As of transformers v4.44, default chat template is no longer "
+            "allowed, so you must provide a chat template if the tokenizer "
+            "does not define one."
+        ),
+    )
+
+    chat_template_kwargs: dict[str, Any] | None = Field(
+        default=None,
+        description=(
+            "Additional keyword args to pass to the template renderer. "
+            "Will be accessible by the chat template."
+        ),
+    )
+
+    @model_validator(mode="before")
+    @classmethod
+    def check_generation_prompt(cls, data):
+        if data.get("continue_final_message") and data.get("add_generation_prompt"):
+            raise ValueError(
+                "Cannot set both `continue_final_message` and "
+                "`add_generation_prompt` to True."
+            )
+        return data

vllm/entrypoints/pooling/classify/protocol.py

@@ -10,9 +10,9 @@ from pydantic import (
 
 from vllm import PoolingParams
 from vllm.config.pooler import get_use_activation
-from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
 from vllm.entrypoints.openai.engine.protocol import OpenAIBaseModel, UsageInfo
 from vllm.entrypoints.pooling.base.protocol import (
+    ChatRequestMixin,
     CompletionRequestMixin,
     PoolingBasicRequestMixin,
 )
@@ -45,48 +45,8 @@ class ClassificationCompletionRequest(PoolingBasicRequestMixin, CompletionReques
         )
 
 
-class ClassificationChatRequest(PoolingBasicRequestMixin):
-    messages: list[ChatCompletionMessageParam]
-
+class ClassificationChatRequest(PoolingBasicRequestMixin, ChatRequestMixin):
     # --8<-- [start:chat-classification-extra-params]
-    add_generation_prompt: bool = Field(
-        default=False,
-        description=(
-            "If true, the generation prompt will be added to the chat template. "
-            "This is a parameter used by chat template in tokenizer config of the "
-            "model."
-        ),
-    )
-
-    add_special_tokens: bool = Field(
-        default=False,
-        description=(
-            "If true, special tokens (e.g. BOS) will be added to the prompt "
-            "on top of what is added by the chat template. "
-            "For most models, the chat template takes care of adding the "
-            "special tokens so this should be set to false (as is the "
-            "default)."
-        ),
-    )
-
-    chat_template: str | None = Field(
-        default=None,
-        description=(
-            "A Jinja template to use for this conversion. "
-            "As of transformers v4.44, default chat template is no longer "
-            "allowed, so you must provide a chat template if the tokenizer "
-            "does not define one."
-        ),
-    )
-
-    chat_template_kwargs: dict[str, Any] | None = Field(
-        default=None,
-        description=(
-            "Additional keyword args to pass to the template renderer. "
-            "Will be accessible by the chat template."
-        ),
-    )
-
     mm_processor_kwargs: dict[str, Any] | None = Field(
         default=None,
         description=("Additional kwargs to pass to the HF processor."),

vllm/entrypoints/pooling/classify/serving.py

@@ -86,8 +86,8 @@ class ClassificationMixin(OpenAIServing):
                         ChatTemplateContentFormatOption,
                         getattr(self, "chat_template_content_format", "auto"),
                     ),
-                    add_generation_prompt=False,
-                    continue_final_message=False,
+                    add_generation_prompt=chat_request.add_generation_prompt,
+                    continue_final_message=chat_request.continue_final_message,
                     add_special_tokens=chat_request.add_special_tokens,
                 )
                 ctx.engine_prompts = engine_prompts

vllm/entrypoints/pooling/embed/protocol.py

@@ -5,13 +5,12 @@ from typing import Any, TypeAlias
 
 from pydantic import (
     Field,
-    model_validator,
 )
 
 from vllm import PoolingParams
-from vllm.entrypoints.chat_utils import ChatCompletionMessageParam
 from vllm.entrypoints.openai.engine.protocol import OpenAIBaseModel, UsageInfo
 from vllm.entrypoints.pooling.base.protocol import (
+    ChatRequestMixin,
     CompletionRequestMixin,
     PoolingBasicRequestMixin,
 )
@@ -57,57 +56,11 @@ class EmbeddingCompletionRequest(PoolingBasicRequestMixin, CompletionRequestMixi
         )
 
 
-class EmbeddingChatRequest(PoolingBasicRequestMixin):
-    messages: list[ChatCompletionMessageParam]
-
+class EmbeddingChatRequest(PoolingBasicRequestMixin, ChatRequestMixin):
     encoding_format: EncodingFormat = "float"
     dimensions: int | None = None
 
     # --8<-- [start:chat-embedding-extra-params]
-    add_generation_prompt: bool = Field(
-        default=False,
-        description=(
-            "If true, the generation prompt will be added to the chat template. "
-            "This is a parameter used by chat template in tokenizer config of the "
-            "model."
-        ),
-    )
-    continue_final_message: bool = Field(
-        default=False,
-        description=(
-            "If this is set, the chat will be formatted so that the final "
-            "message in the chat is open-ended, without any EOS tokens. The "
-            "model will continue this message rather than starting a new one. "
-            'This allows you to "prefill" part of the model\'s response for it. '
-            "Cannot be used at the same time as `add_generation_prompt`."
-        ),
-    )
-    add_special_tokens: bool = Field(
-        default=False,
-        description=(
-            "If true, special tokens (e.g. BOS) will be added to the prompt "
-            "on top of what is added by the chat template. "
-            "For most models, the chat template takes care of adding the "
-            "special tokens so this should be set to false (as is the "
-            "default)."
-        ),
-    )
-    chat_template: str | None = Field(
-        default=None,
-        description=(
-            "A Jinja template to use for this conversion. "
-            "As of transformers v4.44, default chat template is no longer "
-            "allowed, so you must provide a chat template if the tokenizer "
-            "does not define one."
-        ),
-    )
-    chat_template_kwargs: dict[str, Any] | None = Field(
-        default=None,
-        description=(
-            "Additional keyword args to pass to the template renderer. "
-            "Will be accessible by the chat template."
-        ),
-    )
     mm_processor_kwargs: dict[str, Any] | None = Field(
         default=None,
         description=("Additional kwargs to pass to the HF processor."),
@@ -134,16 +87,6 @@ class EmbeddingChatRequest(PoolingBasicRequestMixin):
     )
     # --8<-- [end:chat-embedding-extra-params]
 
-    @model_validator(mode="before")
-    @classmethod
-    def check_generation_prompt(cls, data):
-        if data.get("continue_final_message") and data.get("add_generation_prompt"):
-            raise ValueError(
-                "Cannot set both `continue_final_message` and "
-                "`add_generation_prompt` to True."
-            )
-        return data
-
     def to_pooling_params(self):
         return PoolingParams(
             truncate_prompt_tokens=self.truncate_prompt_tokens,

vllm/entrypoints/pooling/pooling/serving.py

@@ -144,10 +144,8 @@ class OpenAIServingPooling(OpenAIServing):
                     request.messages,
                     chat_template=request.chat_template or self.chat_template,
                     chat_template_content_format=self.chat_template_content_format,
-                    # In pooling requests, we are not generating tokens,
-                    # so there is no need to append extra tokens to the input
-                    add_generation_prompt=False,
-                    continue_final_message=False,
+                    add_generation_prompt=request.add_generation_prompt,
+                    continue_final_message=request.continue_final_message,
                     add_special_tokens=request.add_special_tokens,
                 )
             elif isinstance(request, PoolingCompletionRequest):